Systems and methods for classifying flying insects

ABSTRACT

Systems, apparatuses, and methods of classifying flying insects. The methods utilize recording the wingbeat frequency and amplitude spectrum of flying insects and comparing them to known or created insect models to properly classify the flying insect. The error rate of the classification is reduced by utilizing multiple inputs to the classification system, which may include a precise circadian rhythm for the time of year, current environmental conditions, and flight velocity or direction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/212,744, filed on Sep. 1, 2015, titled A SYSTEM TO CLASSIFY THE SEX,SPECIES, AND PHYSIOLOGICAL STATE OF FLYING INSECTS, the teachings ofwhich are expressly incorporated by reference.

STATEMENT RE: FEDERALLY SPONSORED RESEARCH/DEVELOPMENT

Not Applicable

BACKGROUND Field of the Invention

The present disclosure relates generally to systems for identifyingflying insects and more particularly to noninvasive systems thatoptically record the wingbeat frequency of flying insects and convertthe optical record to a sound file for accurate characterization andanalysis of the recorded flying insect.

Description of the Prior Art

Flying insects have been bother mankind since the beginning of time,whether by destroying agricultural crops, spreading blood bornediseases, or simply interfering with outdoor activities. In order tomitigate these nuisances, it can be critically important to firstclassify the flying insects that are present in a given location, sothat they can be properly managed, and/or for use in entomologicalresearch. Having an inexpensive, noninvasive system capable ofaccurately classifying the flying insects would allow for numerousadvances in agricultural and medical applications, as well as in pureentomological research applications.

The idea of automatically classifying insects using the incidental soundof their flight (as opposed to deliberate insect sounds produced bystridulation) dates back to the very dawn of computers and commerciallyavailable audio recording equipment. In 1945, three researchers at theCornell University Medical College, Kahn, Celestin and Offenhauser, usedequipment donated by Oliver E. Buckley (then President of the BellTelephone Laboratories) to record and analyze mosquito sounds. Theseauthors later wrote, “It is the authors' considered opinion that theintensive application of such apparatus will make possible the precise,rapid, and simple observation of natural phenomena related to the soundsof disease-carrying mosquitoes and should lead to the more effectivecontrol of such mosquitoes and of the diseases that they transmit.”

Since then, there have been sporadic efforts at flying insectclassification from audio features, especially in the last decade;however, little real progress seems to have been made. The lack of priorprogress may be due in part to the fact that most researchers have usedacoustic microphones. Sound attenuates according to an inverse squaredlaw. For example, if an insect flies just three times further away fromthe microphone, the sound intensity (informally, the loudness) drops toone ninth. Any attempt to mitigate this by using a more sensitivemicrophone invariably results in extreme sensitivity to wind noise andto ambient noise in the environment. Moreover, the difficulty ofcollecting data with such devices seems to have led some researchers toobtain data in unnatural conditions. For example, nocturnal insects havebeen forced to fly by tapping and prodding them under bright halogenlights; insects have been recorded in confined spaces or under extremetemperatures. In some cases, insects were tethered with string toconfine them within the range of the microphone. It is hard to imaginethat such insect handling could result in data which would generalize toinsects in natural conditions.

Furthermore, the vast majority of attempts to classify insects by theirflight sounds have explicitly or implicitly used just the wingbeatfrequency. However, such an approach is limited to applications in whichthe insects to be discriminated have very different frequencies (such asa butterfly and a mosquito), and is not well suited for real workapplications where there may be multiple insects present with similarwingbeat frequencies.

As such, there is a need for systems and methods for efficiently andeconomically classifying flying insects by species, sex, physiologicalstate, and other characteristics. Moreover, these systems should be ableto classify the flying insects in real-world situations with minimalerror rates. Furthermore, there is a need for systems that are capableof identifying flying insects regardless of wherever in the world theymay be used, at any point in time, and under any environmentalcondition.

BRIEF SUMMARY

The systems and methods described herein are capable of obtaining datafrom flying insects, and classifying them down to the level of the sex,species and/or physiological state. In order to achieve this, insects inflight have the “sound” of their wing beats extracted by an opticalsensor. This is achieved by shining a light (source) at aphototransistor (target) and measuring the change in voltage (orelectrical resistance or other electrical property). With no insectspresent there is no change in voltage. As the flying insect interruptsthe path of light from the source to the target, its shadow causes afluctuation in light intensity which the phototransistor converts into afluctuation in voltage (or other electrical property). Then, ananalog-to-digital converter is used to convert these fluctuations into adigital signal (in essence a sound file). Various sound features areextracted from the sound file, including the frequency spectrum (alsoknown as the energy spectral density). This frequency spectrum may beregarded as the insect's signature. This signature may be truncated atboth ends to remove spurious data that does not reflect informationproduced by the insect.

Because the signature is affected by the humidity/temperature and airpressure in which it was recorded, the signature may undergo processingto normalize to a canonical invariant representation. This may beachieved by having the system record the corresponding humidity,temperature, air pressure, and/or other relevant environmentalconditions. These environmental conditions are then factored into theanalysis to normalize the signature to standard laboratory conditions.This canonical representation is then input into a classifier (forexample, a Bayesian Classifier, but other classifiers may also besuitable). Because classifiers are sensitive to the prior probability ofseeing a particular insect, that information may also be input into theclassifier.

As the prior probability of seeing an insect strongly depends on thetime relative to dusk and dawn (i.e. the circadian rhythm), thecircadian rhythm of the insect may also be input into the classifier. Inparticular, one aspect of the present disclosure is the ability tocreate a circadian rhythm for any possible time of dawn and dusk, givenany two distinct circadian rhythms for a particular insect for aparticular time of dusk and dawn. This system may then be used tocompute the probability of seeing a particular insect at a given time ofday, for any given day of year, at any given location on Earth.

Likewise, the signal produced by an insect depends on the air density(which itself depends on altitude, humidity, temperature and airpressure). By measuring these characteristics at the time of therecording, the present system may correct for such differences caused bydifferences in air density from normal laboratory conditions to reachvastly increased correct identifications as opposed to the prior art.

In accordance with one embodiment of the present disclosure, there iscontemplated a system for classifying flying insects. The systemincludes a power source in electrical connection with an optical insectdetector subsystem. The optical insect detector subsystem features afirst illuminator that emits a first light transmission, a firstphototransistor positioned to receive the first light transmission, asecond illuminator that emits a second light transmission, a secondphototransistor positioned to receive the second light transmission, anda mechanical divider positioned between the first and secondilluminators. The mechanical divider is positioned to block the firstlight transmission from reaching the second phototransistor and to blockthe second light transmission from reaching the first phototransistor.Furthermore, the first and second illuminators are positioned a defineddistance apart from each other.

The system further includes an analog-to-digital converter in electricalcommunication with the first and second phototransistors. Theanalog-to-digital converter is configured to produce a first digitalsignal from a change in voltage of the first phototransistor and toproduce a second digital signal from a change in voltage of the secondphototransistor. The system also includes a recording device configuredto record the first and second digital signals and a classificationsubsystem configured to receive the first and second digital signalsfrom the recording device and to output a flying insect identificationbased upon the first and second digital signals.

The power source may be a battery and the first and second illuminatorsmay be configured to emit at a constant brightness. Further, the firstand second light transmissions may be created so as to not interferewith insect behavior. For example, the first and second lighttransmissions may be invisible to insects and/or may emit no measurableheat. In certain embodiments, the first and second illuminators may belight emitting diodes. More particularly, in certain embodiments, thelight emitting diodes may emit a light with a wavelength ofapproximately 940 nm and may be positioned approximately 1 cm apart fromeach other.

The system may further include an environmental sensor subsystem. Thisenvironmental sensor subsystem may feature at least one sensorconfigured to measure at least one environmental condition data pointsurrounding the system. Furthermore, the environmental subsystem may bein electronic communication with the recording device, such that therecording device records the environmental condition data pointsmeasured by the sensors and relays said recorded data points to theclassification subsystem. The sensors may measure environmentalconditions such as, but not limited to, the humidity surrounding thesystem, the temperature surrounding the system, the air pressuresurrounding the system, the ambient light surrounding the system, thecurrent date, the current time, and/or the current location of thesystem.

The recording device may record the first and second digital signals asan audio file. More particularly, in certain embodiments, the audio fileis a stereo audio file and the first digital signal is recorded as aleft track and the second digital signal is recorded as a right track.The recording device may further record an amplitude spectrum of thefirst and second digital signals.

The system may further include a circadian rhythm subsystem. Thiscircadian rhythm subsystem may be configured to calculate a circadianrhythm of an insect intended to be identified by the system based uponat least one environmental condition data point measured by theenvironmental sensor subsystem. Additionally, or alternatively, thesystem may further include an actuation subsystem electronicallyconnected to the classification subsystem and configured to actuate adevice upon receiving an identification signal from the classificationsubsystem. Examples of devices that may be actuated include, but are notlimited to, a laser configured to kill an insect based upon receivingthe identification signal, an electromagnetic capture device configuredto capture an insect based upon receiving the identification signaland/or a solenoid valve configured to open and release a chemical basedupon receiving the identification signal. The capture device maycomprise a cup attached to an electromagnet or an electromagneticallyactivated door. Exemplary chemicals released by the solenoid valveinclude insect attractants, insect repellents, pesticides, andlarvicides.

Another embodiment contemplated by the present disclosure is anapparatus for classifying flying insects. The apparatus features a powersource in electrical connection with an optical insect detector unit.The optical insect detector unit contains a first light emitting diodethat emits a first light transmission having a wavelength ofapproximately 940 nm, a first phototransistor positioned to receive thefirst light transmission, a second light emitting diode that emits asecond light transmission having a wavelength of approximately 940 nm,and a second phototransistor positioned to receive the second lighttransmission. The first and second light emitting diodes are positionedapproximately one centimeter apart from each other. The optical insectdetector unit further includes a mechanical divider positioned betweenthe first and second light emitting diodes, such that the mechanicaldivider blocks the first light transmission from reaching the secondphototransistor and blocks the second light transmission from reachingthe first phototransistor.

The apparatus further includes an analog-ti-digital converter inelectrical communication with the first and second phototransistors. Theanalog-to-digital converter is configured to produce a first digitalsignal from a change in voltage of the first phototransistor and toproduce a second digital signal from a change in voltage of the secondphototransistor. The apparatus also features a recording deviceconfigured to record the first and second digital signals as stereotracks of an audio file and an environmental sensor unit. Theenvironmental sensor unit has at least one sensor configured to measureat least one environmental condition data point surrounding the system.Environmental conditions to be measured include, but are not limited to,humidity, temperature, air pressure, ambient light, current date,current time, and current location. The environmental sensor unit is inelectronic communication with the recording device, such that therecording device records the environmental condition data pointsmeasured by the sensors.

The apparatus also includes a circadian rhythm unit configured tocalculate a synthetic circadian rhythm of the insect intended to beidentified by the apparatus. The synthetic circadian rhythm is basedupon at least one environmental condition data point measured by theenvironmental sensor unit, such as the location and/or current date.

The apparatus also has a classification unit configured to receive thefirst and second digital signals from the recording device and theenvironmental condition data points. The classification unit outputs aflying insect identification based upon the first and second digitalsignals, at least one environmental condition data point, and thecalculated synthetic circadian rhythm. The apparatus may be a standalonedevice or may be contained within or attached to an insect trap.

Another embodiment envisioned by the present disclosure includes methodsof classifying flying insects. These methods include the steps ofrecording a first set of data of the life cycle of a flying insect atset environmental conditions utilizing a first day and night cycle. Thisdata includes at least the insect's wingbeat frequency, amplitudespectrum, and circadian rhythm and recording a second set of data of thelife cycle of the flying insect at set environmental conditionsutilizing a different, second day and night cycle. Similarly, the datarecorded includes at least the insect's wingbeat frequency, amplitudespectrum, and circadian rhythm. Then, a data model of the flying insectis created based upon the data recorded in the earlier steps. This datamodel is then input into a classifier. After that, environmentalconditions are recorded where the flying insect is to be classified. Asynthetic circadian rhythm is then created based upon the data model andthe recorded environmental conditions.

A first signal containing the wingbeat frequency and amplitude spectrumof the flying insect to be classified is recorded and a second signalcontaining the wingbeat frequency and amplitude spectrum of the flyinginsect to be classified is also recorded. The second signal is capturedat a known distance from the first signal. By doing so, one is able tocalculate the approximate velocity and flight direction of the insect.The first and second signals are then processed to normalize the signalsbased on the synthetic circadian rhythm and the current environmentalconditions. These processed signals are then input into the classifier,which classifies the insect based upon the inputted signals and the datamodel.

The method may be utilized to classify the insect based on species, sex,or wing variation. Furthermore, the environmental conditions may be usedto compensate for air density differences between those of the model andthose of current conditions, and the resulting changes to the amplitudespectrum currently recorded from those recorded in the initial datarecording steps. Moreover, the method may further include operating anactuator based upon the classification results.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the various embodimentsdisclosed herein will be better understood with respect to the followingdescription and drawings, in which like numbers refer to like partsthroughout, and in which:

FIG. 1 is a schematic view of the systems and apparatuses of the presentdisclosure; and

FIG. 2 is a flowchart of the methods described in the presentdisclosure.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofthe presently preferred embodiment of the invention, and is not intendedto represent the only form in which the present invention may beconstructed or utilized. The description sets forth the functions andsequences of steps for constructing and operating the invention. It isto be understood, however, that the same or equivalent functions andsequences may be accomplished by different embodiments and that they arealso intended to be encompassed within the scope of the invention.

One aspect of the present disclosure is a system that can non-evasivelycapture signals from flying insects, and use this information toclassify the insects by species, physiological state, and/or sex (if theinsect is sexually dimorphic). This system can be a standalone device,or can be integrated into the entrance of, or within, a standard insecttrap. Examples of physiological state include both natural physiologicalstates and those induced by researchers. An example of a naturalphysiological state that can be differentiated by the presentlydisclosed systems is whether a female mosquito has or has not recentlytaken a blood meal. Whereas an example of an induced physiologicalstate, is when a researcher has deliberately removed part of the wing(s)of an insect. It is known in the art that a large area of insect wingscan be removed with minimal effect on load-lifting ability. Furthermore,it is known that combinatorial wing damage patterns can be utilized tomark an insect. For example, the anal veins of a left wing on an insectmay be removed, while the radius of a right wing of the insect may alsobe removed. Since the systems described herein are capable ofdifferentiating the slight changes in flight behavior caused by suchintentional marking of the insect's wings, they may be very useful inmark-and-recapture applications. Mark-and-recapture is frequently usedto estimate, among other things, the size of a population of insectswithin an area.

One example embodiment system 10 is schematically shown in FIG. 1, andincludes an optical insect detector subsystem 12. The optical insectdetector subsystem 12 includes a plurality of light sources (orilluminators) 14 a,b and an equal number of phototransistors 16 a,b. Theoptical insect detector subsystem 12 is electrically connected to, andpowered by a, a power source 18. The power source 18 may be, forexample, a battery. In order to eliminate false readings, theilluminators 14 a,b should produce a constant brightness. Anotherimportant consideration, for best results, is that the illuminators 14a,b (or the heat they produce) should not affect the behavior of theinsects. Accordingly, a suitable light source is, for example, an LEDthat emits light at a wavelength of at, or around, 940 nm (infrared).This wavelength is not visible to insects (or humans) and does notproduce any detectable heat.

A first illuminator 14 a and a first phototransistor 16 a face eachother from some distance, such that some of the light 20 a that isemitted from the first illuminator 14 a falls upon the firstphototransistor 16 a. In this embodiment, the optical insect detectorsubsystem 12 utilizes two illuminators 14 a,b and two correspondingphototransistors 16 a,b, which are placed in a parallel, side-by-sidearrangement, some small distance apart, such that the light 20 a fromthe first illuminator 14 a only falls upon the first phototransistor 16a, and light 20 b from a second illuminator 14 b falls only upon asecond phototransistor 16 b. In this embodiment, the illuminators 14 a,bare placed approximately one centimeter apart. In the absence ofinsects, or anything else, that blocks part of the light 20 a,b, theamount of light 20 a,b that falls on the phototransistors 16 a,b isconstant. Furthermore, by examining the timing difference between whenan insect crosses the first light beam 20 a and the second light beam 20b, the system 10 can infer the speed and direction of travel of theinsect. The speed and direction of travel are two inputs that may be fedinto the system's classification algorithms to identify the flyinginsect.

The phototransistors 16 a,b are connected to an analog to digitalconverter 22, and in a constant light field, no signal 23 (not shown) isproduced. However, when an object blocks all or part of the light 20 a,bfrom the illuminators 14 a,b, the phototransistors 16 a,b will create achanging voltage that the analog to digital converter 22 will convert toa signal 23. The signal 23 produced by the analog to digital converter22 is then captured by a recording device 24. While this signal 23 isrecorded optically as described above, it is effectively a sound file,and can be saved in any standard computer sound file format (e.g., as aWAV file), and if played on a standard audio device sounds like aconventionally recorded sound file recorded with a microphone.Furthermore, since this embodiment utilizes two illuminators 14 a,b andtwo phototransistors 16 a,b, two separate signals 23 a,b will beproduced by the analog to digital converter 22, which can be recorded asseparate stereo tracks by the recording device 24.

The system 10 continuously records and monitors the two independentsignals 23 a,b. In the absence of insects there should be no signal 23produced. In practice, however, electronic devices always produce a tinyamount of signal due to noise in the circuit (for example, wires withinthe electronics may act as a weak antenna, picking up a tiny amount ofradio signals or other electromagnetic interference). Accordingly, thesystem 10 will typically utilize a threshold to determine if the deviceis currently seeing a signal 23 caused by the presence of an insect ornot. This threshold can be computed in several ways. For example, thedevice can be scaled so that there are no insects present, at whichpoint the mean and standard deviation of the noise amplitude ismeasured. In one embodiment, the signal threshold is then set to be themeasured mean plus four standard deviations.

In use, when an insect flies past the two light beams 20 a,b, the analogto digital converter 22 will produce signals 23 a,b whose amplitudegreatly exceed the threshold. The recording device 24 then records asound snippet of the interception event. In certain embodiments, thesound snippet is recorded from one second before the interception, toone second after the interception. The system 10 is able to record asound snippet of this event one second before it happens, by maintainingthe signals 23 a,b in a circular buffer. That is, the recording device24 is constantly recording the signals 23 a,b in a constant loop, andwhen an interception event occurs, the recording is tagged to begin atone second prior in the recording loop.

The system 10 further utilizes sensors 26 to measure environmentalconditions surrounding the system 10. Examples of information that maybe measured by the sensors 26 include, but are not limited to, thehumidity, temperature, air pressure, ambient light, date, time, andlocation on Earth. The sensors 26 are in electrical communication withthe recording device 24, such that information measured by the sensors26 is sent to be recorded by the recording device 24.

The recording device 24 is therefore able to create a holistic record ofthe insect interception event that may include, among other things, astereo sound file containing the signals 23 a,b, assigned to specificstereo tracks; humidity; temperature; air pressure; ambient light; date;time; location on Earth, and/or the amplitude spectrum derived from thestereo sound file. The amplitude spectrum is a vector of real numbers,and provides an additional important piece of information useful indifferentiating one species from another that have similar wingbeatfrequencies. By additionally recording and analyzing the amplitudespectrum, error rates in correctly identifying the species aredramatically reduced. This is similar to the scenario wherein anindividual can differentiate middle C played on a piano and middle Cplayed on a violin. While middle C on both instruments has the samefrequency, additional information is conveyed beyond merely thefrequency. This additional information contained within the amplitudespectrum leads to a dramatic decrease in error rate from simply usingthe wingbeat frequency alone. In one embodiment, values that correspondto frequencies less than 20 Hz or greater than 2000 Hz are eliminated,since such data is very unlikely to contribute true signal generated byan insect. This information may be recorded in various sound fileformats, for example WAV or MP3 formats. Furthermore, when recording thedata, the two signals 23 a,b may be recorded as stereo sound tracks andthe remaining data may be embedded within the sound file as metadata.

Offline Learning and Insect Models

While the identification system described above is greatly improved overthat of the prior art, it still may face potential problems whenattempting to identify unrelated flying insects that have similarwingbeat frequencies. However, with each new piece of information thatcan be provided to the classifier, the error rate is greatly reduced.

Any attempt to classify flying insects benefits from having high qualitydata models of the insect's flight behavior, and the prior probabilityof seeing that sex or species of insect at a given time, on a given dayof the year at a given location on Earth. Moreover, it is known thatinsect's flight behavior depends on environmental conditions. However,under prior art practices, given all these variables, obtaining highquality data models that span the space of all possibilities has provento be difficult, if not impossible. The methods of the presentdisclosure mitigate this problem with the addition of an offlinelearning step 28 and insect modeling steps 30 a,b.

This offline learning step 28 can be achieved by placing a system 10into an insectary (not shown) with juvenile insects of a single species,and single sex and species for sexually dimorphic insects (i.e. pupa formosquitoes, maggots for flies, bees and wasps, etc.) and any food, wateror other items needed to sustain healthy adult insect. The system 10 isoperated continuously to record the emerging insects, 24 hours a day,for their entire adult lives.

This recording is conducted at a fixed humidity, temperature, and airpressure, with a fixed artificial day and night cycle (e.g., 16 hours ofdarkness, followed by 8 hours of light, with a five minute linearramp-up/ramp-down of the brightness to simulate dawn and dusk). Asimilar recording is conducted utilizing a different fixed artificialday and night cycle (e.g., 12 hours of darkness followed by 12 hours oflight), with otherwise the same environmental conditions as the firstrecording.

Collecting data in the manner allows the creation of excellent insectmodels regarding the circadian rhythm 30 a of the insect, for thoserecorded environment conditions. However, the real world does notnecessarily operate under such ideal conditions. As such, the methods ofthe present disclosure allow for the generalization from the recordedconditions in the offline learning step 28 to any reasonable singleenvironment condition 30b, as shown in the flowchart of FIG. 2.

Generalizing to Achieve Invariance to the Circadian Rhythm

The probability that a given insect will be flying at a given time ofday (relative to sunrise/sunset) can be encoded in a flight activitycircadian rhythm. Certain embodiments of systems and methods describedherein may include a circadian rhythm subsystem 32 that can create aninsect specific circadian rhythm for any day of the year, for anylocation on Earth, after seeing just two distinct example circadianrhythms for that insect in the offline learning step 28. Because thisprior probability can vary by orders of magnitude over the course of theday, it greatly affects the ability to correctly identify insects.

For example, the daily flight rhythms of Culex tarsalis and Aedesaegypti are very different, and offer a useful feature for the Bayesianclassifier used in one embodiment of the system 10. To show how thiswould be utilized, an insect detected at 3 am is about four times morelikely to be a Cx. tarsalis, whereas an insect spotted at midday isalmost certainly an Ae. aegypti.

The circadian rhythm subsystem 32, given two distinct circadian rhythmsobserved during the offline learning step 28, can produce a high qualityapproximation to the circadian rhythm that would be observed for anytiming of dawn and dusk via the circadian rhythm modeling step 30 a. Thesystem 10 creates a synthetic circadian rhythm in the circadian rhythmmodeling step 30 a using a type of inverted Dynamic Time Warping (DTW)based on the information provided from the offline learning step 28.Normally, the DTW algorithm takes as input two time series, C and Q, andproduces a warping path in an alignment matrix as a side effect ofcalculating the distance DTW(Q,C). In contrast, the system 10 uses amodified DTW algorithm to align the two given two distinct circadianrhythms to the desired circadian rhythm (the latter specified by desiredtime of dawn and dusk). The amount of x-axis “warping” needed to convertthe observed circadian rhythms into the desired circadian rhythm is usedas an input into a regression algorithm, which is then used to set they-axis value of the circadian rhythm. Accordingly, the circadian rhythmsubsystem 32 takes the synthetic circadian rhythm produced in step 30 aand the captured signals 23 a,b and feeds this information to theclassifier 34.

Generalizing to Achieve Invariance to the Humidity Temperature and AirPressure

Many research papers over the last four decades have examined how insectflight (usually, just considering the wingbeat frequency) varies withaltitude, humidity, temperature or air pressure. However, all of theabove mostly affect the flight behavior by affecting the density of theair. The density of the air for a given temperature or air pressure canbe computed using the ideal gas law. Additionally, if it is desired tocompensate for humidity (humid air is less dense than dry air) thedensity of humid air may be calculated as a mixture of ideal gases. Asit is known that air density affects the amplitude spectrum, this can beadjusted to compensate for the differences in the ambient environmentfrom ideal test conditions.

Given the above, in the offline learning step 28, data is collectedabout various insects at at least one canonical set of environmentalconditions (typically, temp=80° F., Humidity=20%, light cycle 16|8).This data is then utilized in an environmental conditions modeling step30 b to create an environmental conditions subsystem 36. Accordingly,the captured signals 23 a,b and information provided from theenvironmental sensors 26 can be input into the environmental conditionssubsystem 36 to render data that has been compensated based on theenvironmental conditions to match those of ideal test conditions.Alternatively, one can collect data about various insects at ambientenvironmental conditions and normalize it to reflect what would havebeen observed at the canonical set of environmental conditions. Thecaptured signals 23 a,b as modified by the environmental conditionssubsystem 36 are then fed to the classifier 34.

Usage

The information gathered from the offline learning step 28, includingamplitude spectrum, wingbeat frequency, daily flight rhythms, averageflight speed and/or direction, etc. is used to build the classifier 34.While any classifier may be used, in certain embodiments a BayesianClassifier is utilized.

During deployment, the system 10 is placed in the desired location andswitched on. After the insect signals 23 a,b are captured, the system 10normalizes the signals 23 a,b to achieve invariance to ambient altitude,humidity, temperature and air pressure with the environmental conditionssubsystem 36, and to the current time of dawn/dusk with the circadianrhythm subsystem 32. The output from these subsystems 32, 36 is theninput into the classifier 34, which produces the predicted class labelof the sex and/or species and/or physiological state.

Given that the system 10 is able to classify the sex, species, andphysiological states of insects in real time, the system 10 mayoptionally use the ability to classify a single insect, or thecumulative number/ratios of classified insects observed thus far, tocontrol an actuator 38. For example, an actuator 38 can be used toselectively kill insects based on their sex. This is a potentiallyuseful ability to support sterile insect technique (SIT) for mosquitoes.In such cases you want to release only males from a trap or insectary.However, other embodiments could allow for the actuator 38 to kill onlya particular pest insect while allowing a beneficial insect to escapethe trap unharmed. An exemplary kill method would utilize a powerfullaser aimed at the exit of a trap or insectary, wherein the actuator 38activates the laser to kill the desired insect. Upon attempting to exitthe trap, the insect is classified via the system 10, and if it isdetermined to be a target insect, the actuator 38 activates the laser.When a non-target insect is identified, the system 10 does not activatethe actuator 38.

In another embodiment, rather than killing a target insect, the actuator38 may be utilized to capture the target insect. In this case, thesystem 10 is attached to an apparatus that can capture living insects,such as a simple plastic cup suspend over a flat surface. The cup may beheld up by an electromagnet connected to the actuator 38. When a targetinsect is identified by the system 10, a signal is sent to the actuator38 to turn off the electromagnet, causing the cup to drop on top of thetarget insect, thereby capturing it. Alternatively, the actuator 38could close a door in a trap, thereby retaining the target insect insidethe trap.

In yet another embodiment, the system 10 is used to control at least onevalve solenoid. The solenoid may be opened by the actuator 38 to releasechemicals in response to the identification of a single insect, or thecumulative number/ratios of identified insects observed thus far.Examples of chemicals that may be released include, but are not limitedto, attractants, repellents, pesticides or larvicides.

Examples of commercial applications of the present systems and methodsinclude:

Agricultural:

Insect pests destroy billions of dollars worth of food each year. Anyattempt to mitigate their harmful effects requires knowing the densityof the species present (and sometimes the sex of the insects). Thepresent systems can provide this species/sex count in real time.

Vector Control Districts:

Most vector control districts spend considerable effort to quantify thedensity of various mosquitoes in their district. Using conventionalmethods, this information can be inaccurate, out-of-date (by days oreven weeks) and expensive to obtain. The present systems can providereal-time accurate information at a very low amortized cost.

Residential Mosquito Control:

There are several companies that sell devices to lure and killmosquitoes. Such devices could be augmented with the present systems, tolet the user know how successful the mosquito traps are in real time.Furthermore, this information could be actionable. For example, it couldhelp the user optimize trap placement, or provide the user withinformation to reschedule an outdoor event if the noted density ofmosquitoes is trending up.

The above description is given by way of example, and not limitation.Given the above disclosure, one skilled in the art could devisevariations that are within the scope and spirit of the inventiondisclosed herein, including various ways of using the actuator 38 tocontrol insect behavior in some way upon identifying a single insect orthe cumulative number/ratios of identified insects, such as capturingthe insect(s), killing the insect(s), and/or luring or repellinginsect(s) with chemicals, sounds, or lights.

Further, the various features of the embodiments disclosed herein can beused alone, or in varying combinations with each other and are notintended to be limited to the specific combination described herein.Thus, the scope of the claims is not to be limited by the illustratedembodiments.

What is claimed is:
 1. A system for classifying flying insects, saidsystem comprising: a power source in electrical connection with anoptical insect detector subsystem, said optical insect detectorsubsystem comprising: a first illuminator that emits a first lighttransmission; a first phototransistor positioned to receive the firstlight transmission; a second illuminator that emits a second lighttransmission; a second phototransistor positioned to receive the secondlight transmission; a mechanical divider positioned between the firstand second illuminators, such that the mechanical divider blocks thefirst light transmission from reaching the second phototransistor andblocks the second light transmission from reaching the firstphototransistor, and wherein the first and second illuminators arepositioned a defined distance apart from each other; ananalog-to-digital converter in electrical communication with the firstand second phototransistors, wherein the analog-to-digital converter isconfigured to produce a first digital signal from a change in voltage ofthe first phototransistor and to produce a second digital signal from achange in voltage of the second phototransistor; a recording deviceconfigured to record the first and second digital signals; and aclassification subsystem configured to receive the first and seconddigital signals from the recording device and to output a flying insectidentification based upon the first and second digital signals.
 2. Thesystem of claim 1, wherein the power source is a battery.
 3. The systemof claim 1, wherein the first and second illuminators emit first andsecond light transmissions of a constant brightness.
 4. The system ofclaim 3, wherein the first and second light transmissions to notinterfere with insect behavior.
 5. The system of claim 4, wherein thefirst and second light transmissions are invisible to insects.
 6. Thesystem of claim 1, wherein the first and second illuminators emit nomeasurable heat.
 7. The system of claim 1, wherein the first and secondilluminators are light emitting diodes.
 8. The system of claim 7,wherein the light emitting diodes emit a light with a wavelength ofapproximately 940 nm.
 9. The system of claim 1, wherein the first andsecond illuminators are positioned approximately 1 cm apart from eachother.
 10. The system of claim 1, further comprising an environmentalsensor subsystem, said environmental sensor subsystem comprising: atleast one sensor configured to measure at least one environmentalcondition data point surrounding the system, wherein the environmentalsubsystem is in electronic communication with the recording device, suchthat the recording device records the at least one environmentalcondition data point measured by the at least one sensor and relays saidrecorded data point to the classification subsystem.
 11. The system ofclaim 10, wherein the at least one sensor measures the humiditysurrounding the system.
 12. The system of claim 10, wherein the at leastone sensor measures the temperature surrounding the system.
 13. Thesystem of claim 10, wherein the at least one sensor measures the airpressure surrounding the system.
 14. The system of claim 10, wherein theat least one sensor measures the ambient light surrounding the system.15. The system of claim 10, wherein the at least one sensor measures thecurrent date.
 16. The system of claim 10, wherein the at least onesensor measures the current time.
 17. The system of claim 10, whereinthe at least one sensor measures the current location of the system. 18.The system of claim 1, wherein the recording device records the firstand second digital signals as an audio file.
 19. The system of claim 18,wherein the audio file is a stereo audio file and the first digitalsignal is recorded as a left track and the second digital signal isrecorded as a right track.
 20. The system of claim 1, wherein therecording device further records an amplitude spectrum of the first andsecond digital signals.
 21. The system of claim 10, further comprising acircadian rhythm subsystem, said circadian rhythm subsystem configuredto calculate a circadian rhythm of an insect intended to be identifiedby the system based upon at least one environmental condition data pointmeasured by the environmental sensor subsystem.
 22. The system of claim1, further comprising an actuation subsystem electronically connected tothe classification subsystem and configured to actuate a device uponreceiving an identification signal from the classification subsystem.23. The system of claim 22, wherein the device is a laser configured tokill an insect based upon receiving the identification signal.
 24. Thesystem of claim 22, wherein the device is an electromagnetic capturedevice configured to capture an insect based upon receiving theidentification signal.
 25. The system of claim 24, wherein the capturedevice comprises a cup attached to an electromagnet.
 26. The system ofclaim 24, wherein the capture device comprises an electromagneticallyactivated door.
 27. The system of claim 22, wherein the device is asolenoid valve configured to open and release a chemical based uponreceiving the identification signal.
 28. The system of claim 27, whereinthe chemical is selected from the group consisting of attractants,repellents, pesticides, and larvicides.
 29. An apparatus for classifyingflying insects, said apparatus comprising: a power source in electricalconnection with an optical insect detector unit, said optical insectdetector unit comprising: a first light emitting diode that emits afirst light transmission having a wavelength of approximately 940 nm; afirst phototransistor positioned to receive the first lighttransmission; a second light emitting diode that emits a second lighttransmission having a wavelength of approximately 940 nm; a secondphototransistor positioned to receive the second light transmission; amechanical divider positioned between the first and second lightemitting diodes, such that the mechanical divider blocks the first lighttransmission from reaching the second phototransistor and blocks thesecond light transmission from reaching the first phototransistor, andwherein the first and second light emitting diodes are positionedapproximately one centimeter apart from each other, an analog-to-digitalconverter in electrical communication with the first and secondphototransistors, wherein the analog-to-digital converter is configuredto produce a first digital signal from a change in voltage of the firstphototransistor and to produce a second digital signal from a change involtage of the second phototransistor, a recording device configured torecord the first and second digital signals as stereo tracks of an audiofile; an environmental sensor unit comprising: at least one sensorconfigured to measure at least one environmental condition data pointsurrounding the system selected from the group consisting of thehumidity, temperature, air pressure, ambient light, current date,current time, and current location, wherein the environmental sensorunit is in electronic communication with the recording device, such thatthe recording device records the at least one environmental conditiondata point measured by the at least one sensor; a circadian rhythm unitconfigured to calculate a synthetic circadian rhythm of an insectintended to be identified by the apparatus based upon at least oneenvironmental condition data point measured by the environmental sensorunit; and a classification unit configured to receive the first andsecond digital signals from the recording device and the at least oneenvironmental condition data point and to output a flying insectidentification based upon the first and second digital signals, at leastone environmental condition data point, and the calculated syntheticcircadian rhythm.
 30. The apparatus of claim 29, wherein the apparatusis contained within or attached to an insect trap.
 31. A method ofclassifying a flying insect comprising the following steps: a) recordinga first set of data of the life cycle of the flying insect at setenvironmental conditions utilizing a first day and night cycle, whereinthe data recorded includes at least the insect's wingbeat frequency,amplitude spectrum, and circadian rhythm; b) recording a second set ofdata of the life cycle of the flying insect at set environmentalconditions utilizing a different, second day and night cycle wherein thedata recorded includes at least the insect's wingbeat frequency,amplitude spectrum, and circadian rhythm; c) creating a data model ofthe flying insect based upon the data recorded in steps (a) and (b); d)inputting the data model created in step (c) into a classifier, e)recording environmental conditions wherein the flying insect is to beclassified; f) creating a synthetic circadian rhythm based upon the datamodel created in step (c) and the recorded environmental conditions instep (e); g) recording a first signal containing the wingbeat frequencyand amplitude spectrum of the flying insect to be classified; h)recording a second signal containing the wingbeat frequency andamplitude spectrum of the flying insect to be classified, wherein thesecond signal is captured at a known distance from the first signal; i)processing the first and second signals recorded in steps (g) and (h) tonormalize the signals based on the synthetic circadian rhythm created instep (f) and the environmental conditions recorded in step (e); j)inputting the first and second signals processed in step (i) into theclassifier, k) classifying the insect based upon the signals inputted instep (j) and the data model input in step (d).
 32. The method of claim31, wherein the classifier is configured to classify the insect based onspecies.
 33. The method of claim 31, wherein the classifier isconfigured to classify the insect based on sex.
 34. The method of claim31, wherein the classifier is configured to classify the insect based onwing variation.
 35. The method of claim 31, wherein the environmentalconditions recorded in step (e) are used to compensate for air densitydifferences in step (i), and the resulting changes to the amplitudespectrum recorded in steps (g) and (h) from those recorded in steps (a)and (b) for the flying insect.
 36. The method of claim 31, furthercomprising the step l) operating an actuator based upon theclassification result of step (k).